@article{rossi_oliveira favretto_grassi_decarolis_cho_hill_soares chvatal_ranjithan_2019, title={Metamodels to assess the thermal performance of naturally ventilated, low-cost houses in Brazil}, volume={204}, ISSN={["1872-6178"]}, DOI={10.1016/j.enbuild.2019.109457}, abstractNote={Building performance simulation [BPS] tools are important in all design stages. However, barriers such as time, resources, and expertise inhibit their use in the early design stages. This study aims to develop, as part of decision-support framework, metamodels to assess the thermal discomfort in a naturally ventilated Brazilian low-cost house during early design. The metamodels predict the degree-hours of discomfort by heat and/or by cold as a function of design parameters for three Brazilian cities: Curitiba, São Paulo, and Manaus. The key design parameters, related with passive design strategies, are building orientation, shading devices position and dimensions, thermal properties of the walls and roof, window-to-wall ratio, and effective window ventilation area. The method consists of three main stages: (i) baseline model development; (ii) Monte Carlo simulation; (iii) multivariate regression. Overall, the metamodels showed R2 values higher than 0.95 for all climates, except the ones predicting discomfort by heat for Curitiba (R2 =0.61) and São Paulo (R2 =0.75). The proposed metamodels can quickly and accurately assess the thermal performance of naturally ventilated low-cost houses. They can be used to guide professionals during the early design stages, and for educational purposes in building design pedagogy.}, journal={ENERGY AND BUILDINGS}, author={Rossi, Michele Marta and Oliveira Favretto, Ana Paula and Grassi, Camila and DeCarolis, Joseph and Cho, Soolyeon and Hill, David and Soares Chvatal, Karin Maria and Ranjithan, Ranji}, year={2019}, month={Dec} }
 @article{hygh_decarolis_hill_ranji ranjithan_2012, title={Multivariate regression as an energy assessment tool in early building design}, volume={57}, ISSN={0360-1323}, url={http://dx.doi.org/10.1016/j.buildenv.2012.04.021}, DOI={10.1016/j.buildenv.2012.04.021}, abstractNote={This paper presents a new modeling approach to quantify building energy performance in early design stages. Building simulation models can accurately quantify building energy loads, but are not amenable to the early design stages when architects need an assessment tool that can provide rapid feedback based on changes to high level design parameters. We utilize EnergyPlus, an existing whole building energy simulation program, within a Monte Carlo framework to develop a multivariate linear regression model based on 27 building parameters relevant to the early design stages. Because energy performance is sensitive to building size, geometry, and location, we model a medium-sized, rectangular office building and perform the regression in four different cities—Miami, Winston-Salem, Albuquerque, and Minneapolis—each representing a different climate zone. With the exception of heating in Miami, all R2 values obtained from the multivariate regressions exceeded 96%, which indicates an excellent fit to the EnergyPlus simulation results. The analysis suggests that a linear regression model can serve as the basis for an effective decision support tool in place of energy simulation models during early design stages. In addition, we present standardized regression coefficients to quantify the sensitivity of heating, cooling, and total energy loads to building design parameters across the four climate zones. The standardized regression coefficients can be used directly by designers to target building design parameters in early design that drive energy performance.}, journal={Building and Environment}, publisher={Elsevier BV}, author={Hygh, Janelle S. and DeCarolis, Joseph F. and Hill, David B. and Ranji Ranjithan, S.}, year={2012}, month={Nov}, pages={165–175} }